Data is the essential requirement to test any hypothesis or problem. For the measurement of data, different scales such as the ordinal scale are used as per the data type. There are four measurement scales.
- The first one is a nominal scale that aims to spot the property of data.
- The second measurement scale is named as ordinal. It focuses on the ranking of data.
- The third scale of measurement is an interval that focuses on order as well as the difference of variables.
- The last scale type is a ratio that has properties of nominal, ordinal and interval scales.
This article aims to discuss the ordinal scale and the guidelines to measure it.
What is the Ordinal Scale of Measurement with Example?
The objective of this scale is clear from its name. Whenever you have to order your data, the use of an ordinal scale is ensured. There are two data types as qualitative and quantitative. The ordinal scale is used for qualitative data. An important thing related to this scale is that it cannot be used to find the data intervals. Whenever you come up with a question of ‘how much’, it will remain unanswered. As per the objective of data measurement, you need to select the correct scale. From objective to objective, the uses of the scale vary. It is not necessary that you have to use an ordinal scale whenever you come up with quantitative data. So, you have to be very careful in data measurement and its sequence. You can hire a dissertation writing service UK to avoid any mishap.
The examples of ordinal scale are mentioned below:
While conducting a survey, the socio-economic details are collected. Here, you can use a scale of measurement as ordinal.
- Qualification level
- Income level
- Satisfaction level
Let’s have a look at the practical examples as well. These examples are as follows:
What is your income status?
What is your satisfaction level with your job?
What is your qualification level?
What is the Best Measure for Ordinal Data?
There are three ways that the ordinal scale can be used:
- Positional measures
- Non-parametric methods
- Evaluation of rank
- Classification method
The ordinal scale can be measured through all of the aforementioned ways.
Let’s talk about Non-parametric methods.
Other than the Chi-sqaure statistic, all other non-parametric methods can be used for the ordinal scale. It includes Mann-Whitney U Test and Wilcoxon T-Test. Furthermore, it includes Kruskal-Wallis H Test, Friedman ANOVA by Ranks and Spearman.
From different measures of positional, non-parametric and classification methods, the median is the best measure for ordinal data.
How is Ordinal Scale Measured?
The ordinal scale can be measured in different ways. Let’s discuss the median as one method to measure ordinal scale in detail. The main objective of the median is to find the middle point of data. All the ordinal data is ranked form. The ranking of data needs to be in ascending order. Ascending order means the ranking should move from low order to high order. For example,
This is the ascending order.
If you rank it as,
This order is descending. So, you have to ensure that the ranking of data is in ascending form.
Now, let’s focus on the median of the data. For finding the median, you have to use a formula for the calculation of the middle value in the data. That middle value would be the median. In order to make it understandable, let’s look at the example given below:
- The speed of a motorbike
- The low value of speed
- The average value of speed
- The high value of speed
For finding the mid-point, you have to use the formula. When data is not in numeric form, then the formula would be as follow:
As per our data, the n=3
Here n refers to the number of entities.
By putting values in the formula, the answer is 2.
The bold value is the median from all the responses.
The low value of speed
The average value of speed
The high value of speed
Can an Ordinal Preference be measured?
Yes, ordinal preference can be measured in terms of ranking. If you are interested in finding the order or rank only, then ordinal preference is best. In this, you can evaluate different categories in terms of their goodness. For example, you have two options for a particular issue. It includes option A and option B. By using ordinal preference, you can evaluate which option is better. As per ordinal preference, you get that option B is better. You can represent it as option B > option A. That is how ordinal preference work. The data is prioritised based on its use or qualities.
On the other hand, if you want to address the question of ‘how much’, then ordinal preference would not work. By continuing the last example, you can understand this aspect too. You have selected option B from options A and B. Now, someone has asked you how much option B is better than option A? This question will remain unanswerable as per ordinal preferences.
For any testing, the collection of data is a must. The way of data measurement can vary based on the objective of the test. The ordinal scale is used very frequently. All of the guidelines related to the ordinal scale are mentioned above. By following these guidelines, you can easily find data calculations using ordinal easy.